Cluster ensemble for gene expression microarray data

Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, similar techniques have been proposed for clustering algorithms. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble when compared to those based on the clustering techniques used individually.